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AWS EC2 Virtualization 2017: Introducing Nitro

Brendan Gregg

Hardware virtualization for cloud computing has come a long way, improving performance using technologies such as VT-x, SR-IOV, VT-d, NVMe, and APICv. At Netflix, we've been using these technologies as they've been made available for instance types in the AWS EC2 cloud. Each dimension has progressed through these stages: 1.

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A look behind the scenes of AWS Lambda and our new Lambda monitoring extension

Dynatrace

Since its introduction by AWS in 2014, AWS Lambda has revolutionized the compute space and boosted the entire serverless movement. since 2017, and many customers have used it with great success while we collected requirements for the next iteration of our Lambda extension. A look under the hood of AWS Lambda.

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AWS EC2 Virtualization 2017: Introducing Nitro

Brendan Gregg

Hardware virtualization for cloud computing has come a long way, improving performance using technologies such as VT-x, SR-IOV, VT-d, NVMe, and APICv. At Netflix, we've been using these technologies as they've been made available for instance types in the AWS EC2 cloud. Each dimension has progressed through these stages: 1.

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AWS re:Invent 2017: How Netflix Tunes EC2

Brendan Gregg

My last talk for 2017 was at AWS re:Invent, on "How Netflix Tunes EC2 Instances for Performance," an updated version of my [2014] talk. WARNING: These tunables were developed in late 2017, for Ubuntu Xenial instances on EC2.** Virtual Memory. We help where we can. schedtool –B PID. vm.swappiness = 0 # from 60. Huge Pages.

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AWS re:Invent 2017: How Netflix Tunes EC2

Brendan Gregg

My last talk for 2017 was at AWS re:Invent, on "How Netflix Tunes EC2 Instances for Performance," an updated version of my [2014] talk. WARNING: These tunables were developed in late 2017, for Ubuntu Xenial instances on EC2.** Virtual Memory. We help where we can. schedtool –B PID. vm.swappiness = 0 # from 60. Huge Pages.

Tuning 52
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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

AWS Graviton2); for memory with the arrival of DDR5 and High Bandwidth Memory (HBM) on-processor; for storage including new uses for 3D Xpoint as a 3D NAND accelerator; for networking with the rise of QUIC and eXpress Data Path (XDP); and so on.

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5 key areas for tech leaders to watch in 2020

O'Reilly

Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage. there’s a Python library for virtually anything a developer or data scientist might need to do.